Data mining in brain imaging. Export

@article{Megalooikonomou00data, abstract = {Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task-activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.}, address = {Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA. vasilis@cis.temple.edu}, author = {Megalooikonomou, V. and Ford, J. and Shen, L. and Makedon, F. and Saykin, A.}, citeulike-article-id = {950441}, citeulike-linkout-0 = {http://dx.doi.org/10.1177/096228020000900404}, citeulike-linkout-1 = {http://smm.sagepub.com/cgi/content/abstract/9/4/359}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/11084713}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=11084713}, doi = {10.1177/096228020000900404}, issn = {0962-2802}, journal = {Stat Methods Med Res}, keywords = {brain-analysis, data-analysis, data-mining, survey}, month = {August}, number = {4}, pages = {359--394}, posted-at = {2009-02-20 10:25:43}, priority = {4}, title = {Data mining in brain imaging.}, url = {http://dx.doi.org/10.1177/096228020000900404}, volume = {9}, year = {2000} }

TY - JOUR ID - Megalooikonomou00data L3 - citeulike-article-id:950441 N2 - Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task-activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed. IS - 4 JF - Stat Methods Med Res SN - 0962-2802 AD - Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA. vasilis@cis.temple.edu EP - 394 TI - Data mining in brain imaging. VL - 9 SP - 359 KW - brain-analysis KW - data-analysis KW - data-mining KW - survey AU - Megalooikonomou, V AU - Ford, J AU - Shen, L AU - Makedon, F AU - Saykin, A PY - 2000/08// UR - http://dx.doi.org/10.1177/096228020000900404 ER -